Here it is:
Sample getitem_ with some transpose:
    def __getitem__(self,index): #index is patient ID
        'Generate one sample of data'
        ID = self.list_IDs[index]
        MR = np.asarray(nib.load(self.data_dir+ '/MRimages.img').get_data()[:,:,ID])
        CT = np.asarray(nib.load(self.data_dir+ '/CTimages.img').get_data()[:,:,ID])
        
        hMR, wMR = MR.shape[:2]
        new_hMR, new_wMR = 200,200

        top = np.random.randint(0, hMR - new_hMR)
        left = np.random.randint(0, wMR - new_wMR)

        MR = MR[top: top + new_hMR,
                      left: left + new_wMR]

        CT = CT[top: top + new_hMR,
                      left: left + new_wMR]
        
        flip = np.random.randint(2)
        if flip:
            MR.transpose((1,0))
            CT.transpose((1,0))

        sample = {'MR': MR,'CT': CT}
        #if self.transform:
            #sample = self.transform(sample)

        return sample

Give me: torch.Size([64, 200, 200])